Developing Data Science Approaches to Improve Paediatric Critical Care Patient Flows and its Related Health Economics Benefits in Scotland

儿科重症监护（PCC）是向危重儿童提供卫生保健的一项基本服务。在苏格兰，PCC服务由两个儿科重症监护病房（pcccu）提供；一个在爱丁堡，一个在格拉斯哥。近年来，对PCC床位的需求不断增加，据报道出现了PCC床位危机。在重症监护病房床位不足的情况下，必须将患者转移到有床位的病房，或者必须延迟紧急护理，直接影响患者护理。对pcccu病床需求增加的原因包括季节性呼吸道感染、患者出院延误以及pccc患者特征的变化。病床使用情况和人员配备数据通常从pcu收集，以及个别患者水平的数据，包括入院、出院和生理数据。这为开发强大的数据驱动工具提供了一个独特的机会，这些工具可用于优化和改善PCC的患者流程和最终的患者护理。通过pcccu模拟病人流动的能力可以为操作瓶颈和低效率提供有价值的见解。医疗保健领域的仿真模型，如离散事件仿真（DES）、系统动力学（SD）和基于Agent的系统（ABS）等，一经开发，已得到良好的建立，并已证明其益处。与其他通常严重依赖专家意见作为改进方法的方法不同，准确和具有代表性的模拟提供了在整合到医院系统之前测试可能改进方案的机会。近年来，机器学习（ML）加上大型数据集的可用性和计算性能的提高，已经彻底改变了许多领域。在医疗保健领域也不例外，使用ML预测患者护理轨迹成为一个日益活跃的研究领域。随着pcu在个体患者水平上的常规数据的丰富，机器学习模型的开发可以根据患者的生理表型预测患者如何在医院过渡的可能护理轨迹，可以用于规划未来的护理和改善患者流程。本计划将发展一个模拟模型，用以模拟pccu的病人流动，找出瓶颈，然后通过模拟所提出的方案来测试改进的解决方案。其次，将开发机器学习方法，允许从常规收集的临床数据中预测患者护理轨迹，以帮助规划患者护理。最后，它将包括一个彻底的卫生经济分析，以充分量化这种数据驱动的方法带来的影响。参考文献[1]Kusum S Mathews和Elisa F Long。改善重症监护病人流量和床位使用的概念框架。中华胸科杂志，12(6):886-894,2015.[j]Syed Mohiuddin, John Busby, Jelena Savovic, Alison Richards, Kate Northstone, William Hollingworth， Jenny L Donovan和Christos Vasilakis。英国急诊科的病人流量：使用计算机模拟建模方法的系统回顾。中华医学杂志，7(5),2017Eduardo Cabrera, Manel Taboada, Ma Luisa Iglesias， Francisco Epelde和Emilio Luque。基于智能体仿真的医疗急诊科优化计算机科学学报，4 (4):1879 - 1879,2011.[j]Trang Pham, Truyen Tran， Dinh Phung和Svetha Venkatesh。从医疗记录预测医疗轨迹：一种深度学习方法。生物医学信息学报，39 (2):218-229,2017.[j]徐宏腾，吴伟昌，Shamim Nemati，查宏远。通过相互纠正过程的判别学习预测病人流量。知识与数据工程学报，29(1):157-171,2016。

Paediatric Critical Care (PCC) is an essential service providing health care to critically ill children. In Scotland the PCC service is delivered by two Paediatric Critical Care Units (PCCU); one in Edinburgh, and one in Glasgow. In recent years there has been increasing demand for PCCU beds with a PCC bed crisis being reported. In cases where the critical care units run out of available beds, patients must be diverted to a unit with an available bed or urgent care must be delayed directly impacting patient care. Reasons for the increasing demand for PCCU beds range from seasonal respiratory infections to delays in patient discharges as well as changes in PCC patient characteristics. Bed usage and staffing data is routinely collected from the PCCUs as well as individual patient level data including admission, discharge and physiological data. This gives a unique opportunity to develop powerful data driven tools which could be used to optimise and improve patient flow and ultimately patient care in the PCC. The ability to simulate patient flow through a PCCU could provide valuable insights into operational bottlenecks and inefficiencies. Simulation models such as Discrete Event Simulation (DES), System Dynamics (SD) and Agent Based Systems (ABS) within healthcare are well established with proven benefits once developed [1][2][3]. An accurate and representative simulation gives the opportunity to test possible scenarios for improvement before integration into hospital systems unlike other approaches which typically rely heavily on expert opinion as an approach towards improvement. In recent years Machine Learning (ML) coupled with the availability of large datasets and increase in computational performance has revolutionised many domains. In healthcare this is no different, with the prediction of patient care trajectories using ML becoming an increasingly active area of research [4][5]. With the wealth of data routinely available from the PCCUs at an individual patient level, the development of machine learning models which can predict a probable care trajectory for how a patient may transition through the hospital based on a patient physiological phenotype could be employed to plan future care and improve patient flow.This project will develop a simulation model which can be used to simulate patient flow in the PCCUs, identify bottlenecks and then test solutions for improvement through simulating the proposed scenarios. Secondarily machine learning methods will be developed which allow for the prediction of patient care trajectories from routinely collected clinical data to aid in the planning of patient care. Finally, it will include a thorough health economic analysis to fully quantify the impact this data driven approach brings.References[1] Kusum S Mathews and Elisa F Long. A conceptual framework for improving critical care patient flow and bed use. Annals of the American Thoracic Society, 12(6):886-894, 2015.[2] Syed Mohiuddin, John Busby, Jelena Savovic, Alison Richards, Kate Northstone, William Hollingworth, Jenny L Donovan, and Christos Vasilakis. Patient flow within UK emergency departments: a systematic review of the use of computer simulation modelling methods. BMJ open, 7(5), 2017.[3] Eduardo Cabrera, Manel Taboada, Ma Luisa Iglesias, Francisco Epelde, and Emilio Luque.Optimization of healthcare emergency departments by agent-based simulation. Procedia computer science, 4:1880-1889, 2011.[4] Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh. Predicting healthcare trajectories from medical records: A deep learning approach. Journal of biomedical informatics, 69:218-229, 2017.[5] Hongteng Xu, Weichang Wu, Shamim Nemati, and Hongyuan Zha. Patient flow prediction via discriminative learning of mutually-correcting processes. IEEE transactions on Knowledge and Data Engineering, 29(1):157-171, 2016.